Constraints based modeling as a mean to link dialectical ... - IFIP

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Constraints based modeling as a mean to link dialectical ... - IFIP

INSA STRASBOURG GRADUATE SCHOOL OF SCIENCE AND TECHNOLOGY

A R C H I T E C T S + E N G I N E E R S

Constraints based modeling as a mean to link

dialectical thinking and corporate data.

Application to the Design of Experiments

Thomas ELTZER and Roland DE GUIO

roland.deguio@insa-strasbourg.fr

IFIP CAI 2007

IFIP CAI 2007


LGECO : Design engineering laboratory

Domain of research




Modeling and understanding design

Tools for improving design process

Specific knowledge and functions for design

Application areas




Manufacturing systems : material, process and

production systems

Environment : renewable energies, hydraulics (dam,

dikes)

Health : biomechanics (prosthesis joining bones),

robotics

LGECO

Qualification

Engineering

Knowledge

engineering and

computer science

Innovation

management

IFIP CAI 2007


Domain of interests

Development of inventive design methods and computer

supports

Modeling the problem stating and solving reasoning

Analysis and modeling of networks of problems and

contradictions

Etc…

Management of technological innovation

Technological forecasting

Integration of inventive design methods in companies

Organizational and aspects

Methods for engineers

Cognitive and social aspects

IFIP CAI 2007


Initial problem we did address

To what extend is it possible to get a dialectical

model of problems out of several kind of electronic

stored information like :

Patents, scientific papers, other text

Models of systems described by parameters related

together, results of design of experiments,

IFIP CAI 2007


Agenda

1. Dialectical thinking and problem stating /solving

2. Design of experiments results and contradictions stating

3. Constraint programming and contradiction models

4. Discussion and prospects

IFIP CAI 2007


Dialectical philosophical principles

First principle

Everything is permanently in motion and evolving.

These movements and evolutions imply contradictions

and can only exist through them.

Second principle :

To deal with contradictions in models of the world,

empirical knowledge is required connected to the

situation.

IFIP CAI 2007


Dialectic problem solving approach

Method

Stating the problem in the shape of a contradiction

“Solving” the obtained contradiction

Potential add values of this approach

Deep understanding of a problem

Universal shape of problem formulation, interesting in

multidisciplinary context.

Implementation

How to identify deep contradictions ?

How to solve obtained contradictions ?

IFIP CAI 2007


Does dialectic provide general methods to overcome

contradictions?

What about technical systems?

It provides general method but they are not directly

operational in practical situations for technical systems due

to the second principle

Nature is non linear and can be described through

contradictory properties

General approach rules of dialectic reasoning can provide

generic frame for models of contradictions in technical

systems (TRIZ, OTSM etc…).

IFIP CAI 2007


Evaluation parameter EP I

Illustrating dialectical model in design

Px=V1 AND Px = V2

BUT V1 V2 !

Optimization

Px=V

EPI = f 1(….Px ……);

EPII = f2 (….Px …..);

Evaluation parameter EPII

IFIP CAI 2007


REQUIREMENT

OTSM-TRIZ system of contradictions

System/requirement conflict (Administrative)

Contradiction of system between 2 evaluation parameters

(technical)

Contradiction of parameters between 2 values of a

parameter (physical)

CS#1

Visibility


CP

19’’

Weight


LapTop

ScreenSize

14’’

Weight


CS#2

Visibility


IFIP CAI 2007


What does “solving” a contradiction mean?

P y

Py=

K

Px-A

Changing model of system by

cancelling, adding variables and relations

that were not taken into account before

Px

IFIP CAI 2007


2. Design of experiment

1. Dialectical thinking and problem stating /solving

2. Design of experiments results and contradictions stating

IFIP CAI 2007


Design of experiment output

REQUIREMENT

Inputs

X1 X2 X3 … Y1 Y2 Y3 …

I1 -1 -1 -1 1 35 -2 …

I2 -1 -1 1 1 35 -10 …

I3 -1 1 -1 2 26 -3 …

I4 -1 1 1 0 18 -5 …

I5 1 -1 -1 0 18 -3 …

I6 1 -1 1 3 70 0 …

I7 1 1 -1 1 19 -12 …

I8 1 1 1 0 52 1 …

… … … … … … … …

Key

+

REQUIREMENTS

?

Yi

Xi

evaluation parameters

controlled parameters

CP

CS#1

19’’

Visibility

Weight



LapTop

ScreenSize

14’’

Weight


CS#2

Visibility


Assumption: experiments describes all possible output of the system

IFIP CAI 2007


Initial problem situation and assumption (paper)

Situation :

Design of experiment results, but no solution fit

requirements, no optimization approach possible,

Many parameters,

Assumption : No solution to optimization pb Existence of

contradictions

Our Goal: Extract contradictions in the shape of TRIZ system

of contradictions

Motivations :

compare with expert knowledge

Get a deep understanding of problems and their links

use TRIZ/OTSM in order to overcome the contradictions,

IFIP CAI 2007


Recognizing CS and CP contradictions

REQUIREMENT

X1 X2 X3 … Y1 Y2 Y3 … Key

-1 …

1 … Yi evaluation parameters

1 …

-1 … Xi controlled parameters

-1 …

1 … Yi Fit requirements

1 …

1 …

… … … … … … … …

CS situation

X1 X2 X3 … Y1 Y2 Y3 …

-1 …

1 …

1 …

-1 …

-1 …

1 …

1 …

1 …

… … … … … … … …

CS situation

& CP situation

CP

CS#1

19’’

Visibility

Weight



LapTop

ScreenSize

14’’

Weight


CS#2

Visibility


IFIP CAI 2007


Criteria of insolubility and contradiction

Lack of

solution

There exists

contradiction

Counter-example:

No solution, no CS nor CP as defined earlier

X1 X2 Y1 Y2 Y3

-1 -1 10 10 0

-1 1 10 0 10

1 -1 0 10 10

1 1 0 0 0

Conclusion: in order to get a above equivalence for deep contradiction

we propose to change model and definition of contradiction (generalization)

IFIP CAI 2007


Towards generalized contradiction

Inputs

X1 X2 X3 … Y1 Y2 Y3 … Key

I1 -1 -1 -1 1 35 -2 …

I2 -1 -1 1 1 35 -10 … Yi evaluation parameters

I3 -1 1 -1 2 26 -3 …

I4 -1 1 1 0 18 -5 … Xi controlled parameters

I5 1 -1 -1 0 18 -3 …

I6 1 -1 1 3 70 0 … Yi Fit requirements

I7 1 1 -1 1 19 -12 …

I8 1 1 1 0 52 1 …

… … … … … … … …

SY1={I2,I3,I6,I7}

SY2={I4,I5,I7}

SY3={I1,I6,I8}

SY1 ∩ SY2 ∩ SY3=Ø

•Contradiction among k evaluation parameters (EP): generalized CS:

• k=2 there exists( i,j) such that SYi ∩ SYj = Ø

• k=3 there exists (i,j,k) so that SY1 ∩ SY2 ∩ SY3 =Ø

•Criteria for existence of contradiction between EP: there exists a k-uple with

k=


3. Constraint programming and contradictions

1. Dialectical thinking and problem stating /solving

2. Design of experiments results and contradictions stating

3. Constraint programming and contradiction models

1. Motivations

2. Model

IFIP CAI 2007


Motivations

Y1=f1(X1,X2)=-5.X1+5 Eq. 1

Y2=f2(X1,X2)=-5.X2+5 Eq. 2

Y3=f3(X1,X2)=-5.X1.X2+5 Eq. 3

System

-1==6 =requirements

Eq.5

Eq.5

Are there contradictions between requirements and

system?

Is the system consistent?

IFIP CAI 2007


Constraint programming model

Any CSP comprises:

a set of n variables V’ =(V1,..,Vn). A value of Vk is

noted vk. Therefore, v’ is defined as the nuple

(v1,..,vn);

a set of domains D’=(D1,..,Dn) where Di is the finite

set of possible values for Vi;

a set of constraints C’=(C1,..,Cj) between the existing

variables.

A solution to a CSP is an assignment of a value from its

domains to every variable, in such a way that all

constraints are satisfied at once.

A CSP is solvable if at least one solution exists.

IFIP CAI 2007


Case of DoE of example

The domains for X1 and X2 is DX1=DX2=[-1;+1]

The domains for Yi : DY1=DY2=DY3=[6;+∞[.

.

The CSP model is defined by:

the set of five variables. V’=X’UY’=(X1,X2,Y1,Y2,Y3).

X’=(X1,X2); Y’=(Y1,Y2,Y3);

the set of domains: D’=(DY1, DY2, DY3,DX1, DX2).

the set of constraints C’={C1,C2,C3}.

Y1=f1(X1,X2)=-5.X1+5 Eq. 1

Y2=f2(X1,X2)=-5.X2+5 Eq. 2

Y3=f3(X1,X2)=-5.X1.X2+5 Eq. 3

IFIP CAI 2007


Contradiction criterion

REQUIREMENT

Let’s define

S

Y

i

x'

/

y

i

f

i

x'

D

Y

i

AND

x'

D'

X

Then criterion


Y

i / Y Y '

i

S

i

Illustration on the example and meaning Syi:

X 2

1

S Y3

S Y2

ЄS Y1

y 1 Є[6;+∞[

(y 2 ,y 3 )Є[6;+∞[ x [6;+∞[



S Y1

X 1

S Y2.X2

S Y1.X2

-0,2

(x 1 ,x 2 )

Є S Yi

y 2 Є[6;+∞[

(y 1 ,y 3 )Є[6;+∞[ x [6;+∞[

y 3 Є[6;+∞[




-1

-1 -0,2

1

(a)

Є S Yp

(y 1 ,y 2 )Є[6;+∞[ x [6;+∞[

(b)


IFIP CAI 2007


4. Discussion and prospects (a)

Results

SYi ∩ SYj = ØCS in OTSM or TC in TRIZ

There are more sophisticated contradictions than

TRIZ ones

Non existence of TC (TRIZ) does not mean we are in

optimization situation

CSP could become a common model for input of

optimization or model changing approaches

IFIP CAI 2007


4. Discussion and prospects (b)

Consequence

Understanding why it may be difficult for

professionals to find TC if they do not exist

It is also more difficult to conceptualize in the mind

the general contradiction

A way to get contradiction with professional (not with

electronic data) could be to built the table or the CSP

model and extract contradictions

The CSP model clearly shows the problem about

reuse of contradiction network : contradiction

depends on system model +

requirements=>contradictions are requirement

dependent

IFIP CAI 2007


4. Discussion and prospects

Future work

Systematic and efficient algorithm to analyze previous

features (what is engineering, what requires new?)

Better understanding of technology for changing

model within the scope of SCP models (existing

works)

Describing TRIZ and OTSM solving in CSP

IFIP CAI 2007


To be continued…..

IFIP CAI 2007


INSTITUT NATIONAL DES SCIENCES APPLIQUÉES DE STRASBOURG

Fin

S. Dubois & R. De Guio La TRIZ: aperçu – INSA de Strasbourg

Juin 2007

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